The Experts below are selected from a list of 294930 Experts worldwide ranked by ideXlab platform

Susan A. Peters - One of the best experts on this subject based on the ideXlab platform.

  • Developing understanding of statistical variation: secondary statistics teachers’ perceptions and recollections of learning factors
    Journal of Mathematics Teacher Education, 2013
    Co-Authors: Susan A. Peters
    Abstract:

    This retrospective phenomenological study investigates activities and actions identified by secondary statistics teachers who exhibit robust understandings of variation as deepening their understandings of statistical variation. Using phenomenological methods and a frame of Mezirow’s transformation theory, analysis revealed learning factors that include their interests in statistics, motivation to encounter and resolve dilemmas, desires to have an overarching Content Framework, propensities for critical reflection, and actions on opportunities to engage in statistical learning activities and rationale discourse with more knowledgeable others. The extent to which these teachers embrace these opportunities distinguishes them from other teachers. Results from this study provide some basis for formulating hypotheses about secondary teachers’ statistics learning in general by contributing to understanding circumstances that may be conducive to developing deep understandings of statistical Content. This study also advances the use of retrospective methods within a theoretical frame for adult learning to investigate teacher learning.

Wang Yichen - One of the best experts on this subject based on the ideXlab platform.

  • Review of Software Psychology
    Computer Science, 2013
    Co-Authors: Wang Yichen
    Abstract:

    Since software is the production of human cognition,human is the key factor affecting software quality.With extention of "engineering system" to "socio-technical system",software psychology increasingly manifests its implication value to software engineering.Firstly,the developing course and Content Framework of software psychology were summarized.Then the research advances in 6 branches were presented respectively,and the implications for software engineering were provided,and research shortages and clues for future study were analyzed.Finally,the overall developing trends of software psychology were concluded.

Gan Chun-mei - One of the best experts on this subject based on the ideXlab platform.

Berhe Getnet Baraki - One of the best experts on this subject based on the ideXlab platform.

  • 198. The Research Framework of Provincial Digital Water Conservancy
    Tunnelling and Underground Space Technology, 2005
    Co-Authors: Youjing Zhang, Berhe Getnet Baraki
    Abstract:

    The paper deals with the Content, Framework and significance of Provincial Digital Water Conservancy, as it is developing in China. The main components of provincial digital water conservancy include data collection, network construction, digital platform and application system. It offers the decision making support for preventing flood and reducing flood damage, water-resource management, constructing and managing irrigation works and for the sustainable development of the environment, economy and society.

  • The research Framework of Provincial Digital Water Conservancy
    IAHS-AISH publication, 2004
    Co-Authors: Youjing Zhang, Berhe Getnet Baraki
    Abstract:

    The paper deals with the Content, Framework and significance of Provincial Digital Water Conservancy, as it is developing in China. The main components of provincial digital water conservancy include data collection, network construction, digital platform and application system. It offers the decision-making support for preventing flood and reducing flood damage, water-resource management, constructing and managing irrigation works, and for the sustainable development of the environment, economy and society.

Ahmed Al-hmouz - One of the best experts on this subject based on the ideXlab platform.

  • An adaptive Framework to provide personalisation for mobile learners
    2012
    Co-Authors: Ahmed Al-hmouz
    Abstract:

    The advancement of technologies in wireless and hand-held devices, combined with the ability to access learning Content everywhere and anytime, has created significant interest in mobile learning (m-learning) in recent years. New smart phones are capable of exchanging voice, text, pictures and video. In addition, the new wireless network provides high-speed connections at a low cost to mobile users. Mobile learning fulfils the promise of learning ”on the move” by allowing learners to take control over the time and location of their learning. Learning through a mobile device makes learning truly personalised. Learners have the ability to choose learning Content based on their interests, thus making learning learner-centric. In contrast to typical electronic learning (e-learning) products, this access to personalised information means each learner can access the resources they need in a timely manner while minimising wasted bandwidth. Providing immediate access to relevant and interesting information, based on the individual learner’s requirements, encourages use and increases engagement because learners are able to access the information they want wherever they are. Mobile learning is still in its infancy and research indicates that few projects have produced any lasting outcomes. Other research to date has focused on the connectivity problem of using wireless networks. The ultimate goal of this thesis is to present the design and implementation of a Machine Learning Based Framework for Adaptive Mobile Learning, to provide a logical structure for the process of adapting learning Content to satisfy individual learner characteristics by taking into consideration the learner’s needs. One main contribution of this thesis is a novel development Framework in the field of mobile learning. The Framework depicts the process of adapting learning Content to satisfy individual learner characteristics by taking into consideration the learner’s needs. The system architecture of the context adaptation based learner profile Framework is fundamentally grounded on a number of logical layers. This Framework provides a way to reduce the complexity of managing different mobile device settings to enhance learning environments. Delivery options for mobile learning are increasing, however new technologies alone will not improve the experience of mobile learners. There are a number of factors that impact on a typical learning experience, and many more when that learning experience becomes ’mobile’. This thesis presents an Adaptive Mobile Learning Content Framework that describes the factors that play an important role in delivering learning Content to mobile learners, and their relationship with each other. Once the necessary information is collected about a learner either automatically (e.g. location, device, previous usage) or through learner input (e.g. age) learning Content can be adapted to meet the unique and personal needs of that learner within their current context. It allows consideration of individual learning styles and scenarios, device and application capabilities, and material structure, leading to a customisation of the type and delivery format of learning information in response to the learner. Based on the newly developed Frameworks, another major contribution of the thesis is the establishment of an adaptive learner model. Generally speaking, a m-learning adaptive Framework provide personalised services to learners in accordance with their current situation or assumptions about each interacting learner. Adaptive systems adapt their own behaviour to suit and find the optimal outcome for a specific learner’s needs. An efficient adaptive system is capable of deciding autonomously what to deliver, how to do it and when to do it. It is essential for adaptive systems to gather information about the learner. Without such information about the learner, the adaptive system is not able to adapt itself to the learner’s characteristics and preferences. The required information is stored and managed in the form of a learner profile and learner model. The construction of the learner model is another main contribution of this thesis. The vast amount of data involved in any successful adaptation process creates complexity and poses serious challenges. The Enhanced Learner Model focuses on how to model the learner and all possible contexts in an extensible way that can be used for personalisation in mobile learning. The learner model is logically partitioned into smaller elements or classes in the form of a learner profile, which can represent the entire learning process. Learner profile contains learner’s preferences, knowledge, goals, plans, place and possibly other relevant aspects that are used to provide personalised learning Content. This thesis presents a Neuro-Fuzzy model for delivering adapted learning Content to mobile learners. The adaptation of learning Content is based on Adaptive Neuro-Fuzzy Inference System (ANFIS). ANFIS has been recognized for its flexible and adaptive characteristics. ANFIS is a powerful approach to develop fuzzy systems that are capable of learning by providing IF-THEN fuzzy rules in linguistic form. The ANFIS approach is adopted to determine all possible conditions; these cannot be determined by using individual techniquea. The detailed simulation results demonstrated that ANFIS would help the adaptive system to determine a suitable learning Content format.

  • ISTAS - Learning on location: An adaptive mobile learning Content Framework
    2010 IEEE International Symposium on Technology and Society, 2010
    Co-Authors: Ahmed Al-hmouz, Alison Freeman
    Abstract:

    Delivery options for mobile learning are increasing, however new technologies alone will not improve the experience of mobile learners. There are a number of factors that impact on a typical learning experience, and many more when that learning experience becomes ‘mobile’. This paper presents a Framework to describe the factors that play an important role in delivering learning Content to mobile learners, and their relationship to each other. Once the necessary information is collected about a user — either automatically (e.g. location, device, previous usage) or through user input (e.g. age) — learning Content can be adapted to meet the unique and personal needs of that learner within their current context. The learning Content Framework allows consideration of individual learning styles and scenarios, device and application capabilities, and material structure, leading to a customization of the type and delivery format of learning information in response to the user. Ultimately, the personalized response to each user (whether they are working independently or in communication with other learners) improves user engagement and the overall learning experience, as well as saving time.